Bibliographic record
Abstract
It’s funny, I’ve only become more in touch with my Chinese heritage since I became a theatre professional. I grew up in a tiny farming community in southern Alberta, the son of a Chinese shopkeeper, whose own father came to this country to build the railway. My grandfather abandoned the harsh railway life and walked through the Rockies, finally settling in the hamlet of Cluny (population 87), which sits on the bald Alberta prairie about halfway between Calgary and Medicine Hat. As the only Chinese family in such a small town, I spent my youth trying very hard not to be Chinese. My mother was Caucasian, so I gravitated toward her Irish heritage much more than my father’s traditions. Fast-forward several years: upon finishing my theatre training at the University of Calgary, I got my initial professional work because I was Chinese. I was confused . . . suddenly being Chinese was a good thing. I was fortunate to be cast in two separate productions of Madama. Butterfly, one at Alberta Theatre Projects, and then at the Arts Club Theatre in Vancouver, where I now live and work. I got several more parts because of my Chinese background and through that I delved deeper into the history and adventures of the Chinese side of my family; what had been a badge of shame as a child has grown into one of great honour.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.043 | 0.015 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".